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Title: The long-range predictability of European drought
Author: Lloyd-Hughes, Benjamin
ISNI:       0000 0001 3611 6636
Awarding Body: University of London
Current Institution: University College London (University of London)
Date of Award: 2002
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Large-scale European drought is not a rare event yet few attempts have been made to predict the phenomenon. This study is the first to assess the level and origin of forecast skill available from long-range statistical predictions of meteorological drought in Europe. Meteorological drought is defined as a precipitation deficit over an extended period of time. A high spatial resolution, multi-temporal climatology for the incidence of 20th century European drought is developed. The climatology provides, for a given location or region, the time series of drought strength, the number, the mean duration and the maximum duration of droughts of a given intensity, and the trend in drought incidence. The drought climatology is based on monthly Standardised Precipitation Indices (SPIs) calculated on a 0.5" grid over the European region [35°N-70°N, 35°E -10°W] for the period 1901-1999. SPIs standardised over 3 and 12 months are shown to be adequate for describing moisture availability across Europe. A rotated principal component analysis is used to identify six regions of coherent drought behaviour. These regions do not exhibit seasonality in drought incidence. Wavelet transformation is used to investigate the spectral characteristics of the principal components at scales between 1 and 40 years. Significant periodicity at annual-to-decadal time scales is not detected in any of the regional drought signals. Extensive attempts are made to relate the drought climatology to lower boundary layer conditions including sea surface temperature, snow cover and sea ice extent. Spring is found to be the most predictable season for European precipitation and drought. Results from an empirical model show that up to 35% of the variance in springtime SPI over the region [45°N-55°N, 35°E-5°W] can be predicted using a combination of ENSO, local North Atlantic SST forcing and SPI persistence.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available